One-bit Supervision for Image Classification
Authors: Hengtong Hu, Lingxi Xie, Zewei Du, Richang Hong, Qi Tian
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our setting and approach on three image classification benchmarks, namely, CIFAR100, Mini-Image Net and Image Net. ... Results demonstrate the superiority of one-bit supervision, and, with diagnostic experiments, we verify that the benefits come from a more efficient way of utilizing the information of incomplete supervision. |
| Researcher Affiliation | Collaboration | Hengtong Hu1,2, Lingxi Xie3, Zewei Du3 Richang Hong1,2 , Qi Tian3 1Key Laboratory of Knowledge Engineering with Big Data, Hefei University of Technology, 2School of Computer Science and Information Engineering, Hefei University of Technology, 3Huawei Inc. |
| Pseudocode | No | The paper describes the training procedure verbally and with a diagram (Figure 1), but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide an unambiguous statement about releasing source code for the described methodology or a direct link to a code repository. |
| Open Datasets | Yes | We conduct experiments on three popular image classification benchmarks, namely, CIFAR100, Mini-Imagenet, and Imagenet. CIFAR100 [16] contains 50K training images and 10K testing images... For Mini-Image Net in which the image resolution is 84 84, we use the training/testing split created in [28]... For Image Net [3], we use the commonly used competition subset [30]... |
| Dataset Splits | No | The paper describes training and test sets but does not explicitly mention or detail a separate validation dataset split with specific percentages or counts for hyperparameter tuning or model selection. |
| Hardware Specification | Yes | Other hyper-parameters simply follow the original implementation, except that the batch size is adjusted to fit our hardware (e.g., eight NVIDIA Tesla-V100 GPUs for Image Net experiments). |
| Software Dependencies | No | The paper refers to adopted algorithms and network architectures (e.g., Mean-Teacher, ResNet) and states that 'Other hyper-parameters simply follow the original implementation,' but it does not specify software dependencies with version numbers (e.g., PyTorch 1.9, TensorFlow 2.x). |
| Experiment Setup | Yes | We use a 26-layer deep residual network [10] with Shake-Shake regularization [7] for CIFAR100, and a 50-layer residual network for Mini-Image Net and Image Net. The number of training epochs is 180 for CIFAR100 and Mini-Image Net, and 60 for Image Net. The consistency parameter is 1,000 for CIFAR100, and 100 for Mini-Image Net and Image Net. |